Generation of 3D molecules in pockets via a language model

Generative models for molecules based on sequential line notation (for example, the simplified molecular-input line-entry system) or graph representation have attracted an increasing interest in the field of structure-based drug design, but they struggle to capture important three-dimensional (3D) spatial interactions and often produce undesirable molecular structures. To address these challenges, we introduce Lingo3DMol, a pocket-based 3D molecule generation method that combines language models and geometric deep learning technology. A new molecular representation, the fragment-based simplified molecular-input line-entry system with local and global coordinates, was developed to assist the model in learning molecular topologies and atomic spatial positions. Additionally, we trained a separate non-covalent interaction predictor to provide essential binding pattern information for the generative model. Lingo3DMol can efficiently traverse drug-like chemical spaces, preventing the formation of unusual structures. The Directory of Useful Decoys-Enhanced dataset was used for evaluation. Lingo3DMol outperformed state-of-the-art methods in terms of drug likeness, synthetic accessibility, pocket binding mode and molecule generation speed.